fTh1,SSKJr "SS\5rS/rg))PretrainedConfigc^\rSrSrSrSrS/rSU4SjjrSrU=r $) Zamba2Configa This is the configuration class to store the configuration of a [`Zamba2Model`]. It is used to instantiate a Zamba2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Zamba2 model. [Zyphra/Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the Zamba2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Zamba2Model`] max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 54): Number of hidden layers in the model. layers_block_type (`list`, *optional*): List of layer types, which can be either "mamba" or "hybrid". mamba_d_state (`int`, *optional*, defaults to 64): shape of the state space latents. mamba_d_conv (`int`, *optional*, defaults to 4): Size of the convolution kernel. mamba_expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size. mamba_ngroups (`int`, *optional*, defaults to 1): Number of groups for the evolution matrices of mamba 2. time_step_min (`float`, *optional*, defaults to 0.001): Minimum `time_step` used to bound `dt_proj.bias`. time_step_max (`float`, *optional*, defaults to 0.1): Maximum `time_step` used to bound `dt_proj.bias`. time_step_floor (`float`, *optional*, defaults to 0.0001): Minimum clamping value of the `dt_proj.bias` layer initialization. time_step_limit (`tuple`, *optional*): Accepted range of time step values. n_mamba_heads (`int`, *optional*, defaults to 8): Number of heads for the evolution matrices of mamba 2. use_conv_bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the convolution layer of the mixer block. chunk_size (`int`, *optional*, defaults to 256): Size of the chunks that will comprise the sequence. use_mem_eff_path (`bool`, *optional*, defaults to `False`): Whether or not to use the fused conv1d and scan in mamba2 layers. add_bias_linear (`bool`, *optional*, defaults to `False`): Flag indicating whether or not to use bias in various layers intermediate_size (`int`, *optional*, defaults to 4 * hidden_size): Dimension of the MLP representations. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the MLP. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=None`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. num_mem_blocks (`int`, *optional*, defaults to 1): Number of unshared transformer blocks. use_shared_attention_adapter (`bool`, *optional*, defaults to `False`): If True, unshared adapters (formally the same as LoRA but used in the base model) will be added to the q, k, v projectors in the shared attention layers. adapter_rank (`int`, *optional*, defaults to 128): Rank of the adapter in the shared MLP and shared attention layers. use_mem_rope (`bool`, *optional*, defaults to `False`): If True, includes RoPE in the shared attention layers. rope_theta (`float`, *optional*, defaults to `10000.0`): The base period of the RoPE embeddings. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. num_logits_to_keep (`int` or `None`, *optional*, defaults to 1): Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the logits of the last prompt token are needed for generation. For long sequences, the logits for the entire sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint significantly. pad_token_id (`int`, *optional*, defaults to 0): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. use_long_context (`bool`, *optional*, defaults to `False`): Activates the context-extended version of Zamba by modifying RoPE. ```python >>> from transformers import Zamba2Model, Zamba2Config >>> # Initializing a Zamba2-2.7B style configuration >>> configuration = Zamba2Config() >>> # Initializing a model from the Zamba2-2.7B style configuration >>> model = Zamba2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```zamba2past_key_valuesc% Z>[T)U]"S U!U"U#S.U%D6 XlX lX0lUc SU-UlOUUlUUlX@lUUlUUl SU-Ul SUR-UR-Ul UUl UUl U$UlU(a+U$(a$Sn&UU&URURS- - --nUUlX`lXplXlUUlXlXl[-X-5U-UlXlUUlXlUUlUUlXlXlXlU$(aSUlUcUnUUl UUlURUR-Ul!URUl"Uc4S/S/S-S/-S --S/S--S/-S/S --S/-S/S--Ul#OXPl#UUl$UUl%UUl&U Ul'[QURF5V'V(s/sHun'n(U(S:XdMU'PM sn(n'Ul)UUl*gs sn(n'f) N) pad_token_id bos_token_id eos_token_idi@mambahybridr)+super__init__ vocab_sizemax_position_embeddings hidden_sizeintermediate_size hidden_actnum_hidden_layersnum_attention_headsnum_mem_blocksattention_hidden_sizeattention_head_dimattention_dropout use_mem_ropeuse_long_context rope_theta mamba_d_state mamba_d_conv mamba_expandadd_bias_linear mamba_ngroups n_mamba_headsint mamba_headdim use_conv_bias chunk_sizetime_step_limituse_shared_attention_adapter adapter_rank time_step_min time_step_maxtime_step_floornum_key_value_heads kv_channelsnum_query_groupslayers_block_typeinitializer_range rms_norm_eps use_cachenum_logits_to_keep enumeratehybrid_layer_idsuse_mem_eff_path)*selfrrrrr8r%r&r'r)r2r3r4r/r*r-r.r?r(rrrr5r!rr0r1r"r$r9r:r;r<r r r r#kwargsaindextype __class__s* g/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/zamba2/configuration_zamba2.pyrZamba2Config.__init__sP  %%%   %'>$&  $%&_D "%6D "$!2#6 ,%&_""#d&6&6"6$:R:R"R!2( 0 ,A#aD,C,CtG^G^abGb,c&ddJ$*((.** !;< M*$.,H)(**. +0D (  &"5 #6 #6 ++t/G/GG $ 8 8  $ 9q=H:-23)a- *)a-  *  )a-    "&7 "!2(""4:CDDZDZ:[ p:[;5$_cgo_o:[ p 0!qs ? 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